Abstract

Additive manufacturing (AM) uses computer-aided design to construct parts layer by layer. Relative to traditional manufacturing processes, AM provides a time-efficient and cost-effective way to produce low-volume, customized parts with complex geometries. This work presents an improved Cross-Coupled iterative learning control (CCILC) scheme to overcome current limitations in contour following for complex, free-form curves in AM. The approach involves modifying the definition of the error vector used in the individual axis iterative learning controllers and defining time varying weightings based on the curvature of the reference trajectory to couple tracking and contour errors. In this paper, the design for the improved CCILC system is presented, and the performance of this system is compared to the performance of existing ILC control schemes via simulations. In comparison to the current control methods, the simulation results demonstrate significant performance improvements for contour tracking of a reference trajectory with high levels of curvature.

Original languageEnglish (US)
Title of host publicationControl and Optimization of Connected and Automated Ground Vehicles; Dynamic Systems and Control Education; Dynamics and Control of Renewable Energy Systems; Energy Harvesting; Energy Systems; Estimation and Identification; Intelligent Transportation and Vehicles; Manufacturing; Mechatronics; Modeling and Control of IC Engines and Aftertreatment Systems; Modeling and Control of IC Engines and Powertrain Systems; Modeling and Management of Power Systems
PublisherAmerican Society of Mechanical Engineers (ASME)
ISBN (Electronic)9780791851906
DOIs
StatePublished - Jan 1 2018
EventASME 2018 Dynamic Systems and Control Conference, DSCC 2018 - Atlanta, United States
Duration: Sep 30 2018Oct 3 2018

Publication series

NameASME 2018 Dynamic Systems and Control Conference, DSCC 2018
Volume2

Other

OtherASME 2018 Dynamic Systems and Control Conference, DSCC 2018
CountryUnited States
CityAtlanta
Period9/30/1810/3/18

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering

Fingerprint Dive into the research topics of 'Improved cross-coupled iterative learning control for contouring NURBS curves'. Together they form a unique fingerprint.

  • Cite this

    Armstrong, A., Johnson, A. W., & Alleyne, A. (2018). Improved cross-coupled iterative learning control for contouring NURBS curves. In Control and Optimization of Connected and Automated Ground Vehicles; Dynamic Systems and Control Education; Dynamics and Control of Renewable Energy Systems; Energy Harvesting; Energy Systems; Estimation and Identification; Intelligent Transportation and Vehicles; Manufacturing; Mechatronics; Modeling and Control of IC Engines and Aftertreatment Systems; Modeling and Control of IC Engines and Powertrain Systems; Modeling and Management of Power Systems (ASME 2018 Dynamic Systems and Control Conference, DSCC 2018; Vol. 2). American Society of Mechanical Engineers (ASME). https://doi.org/10.1115/DSCC2018-9145